40,267 research outputs found
Analyzing Consistency of Behavioral REST Web Service Interfaces
REST web services can offer complex operations that do more than just simply
creating, retrieving, updating and deleting information from a database. We
have proposed an approach to design the interfaces of behavioral REST web
services by defining a resource and a behavioral model using UML. In this paper
we discuss the consistency between the resource and behavioral models that
represent service states using state invariants. The state invariants are
defined as predicates over resources and describe what are the valid state
configurations of a behavioral model. If a state invariant is unsatisfiable
then there is no valid state configuration containing the state and there is no
service that can implement the service interface. We also show how we can use
reasoning tools to determine the consistency between these design models.Comment: In Proceedings WWV 2012, arXiv:1210.578
Using formal metamodels to check consistency of functional views in information systems specification
UML notations require adaptation for applications such as Information Systems (IS). Thus we have defined IS-UML. The purpose of this article is twofold. First, we propose an extension to this language to deal with functional aspects of IS. We use two views to specify IS transactions: the first one is defined as a combination of behavioural UML diagrams (collaboration and state diagrams), and the second one is based on the definition of specific classes of an extended class diagram. The final objective of the article is to consider consistency issues between the various diagrams of an IS-UML specification. In common with other UML languages, we use a metamodel to define IS-UML. We use class diagrams to summarize the metamodel structure and a formal language, B, for the full metamodel. This allows us to formally express consistency checks and mapping rules between specific metamodel concepts. (C) 2007 Elsevier B.V. All rights reserved
Large scale evaluation of local image feature detectors on homography datasets
We present a large scale benchmark for the evaluation of local feature
detectors. Our key innovation is the introduction of a new evaluation protocol
which extends and improves the standard detection repeatability measure. The
new protocol is better for assessment on a large number of images and reduces
the dependency of the results on unwanted distractors such as the number of
detected features and the feature magnification factor. Additionally, our
protocol provides a comprehensive assessment of the expected performance of
detectors under several practical scenarios. Using images from the
recently-introduced HPatches dataset, we evaluate a range of state-of-the-art
local feature detectors on two main tasks: viewpoint and illumination invariant
detection. Contrary to previous detector evaluations, our study contains an
order of magnitude more image sequences, resulting in a quantitative evaluation
significantly more robust to over-fitting. We also show that traditional
detectors are still very competitive when compared to recent deep-learning
alternatives.Comment: Accepted to BMVC 201
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